Consumer Understanding, Perception and Interpretation of Serving Size Information on Food Labels: A Scoping Review
Bibliographic record
Abstract
The increase in packaged food and beverage portion sizes has been identified as a potential factor implicated in the rise of the prevalence of obesity. In this context, the objective of this systematic scoping review was to investigate how healthy adults perceive and interpret serving size information on food packages and how this influences product perception and consumption. Such knowledge is needed to improve food labelling understanding and guide consumers toward healthier portion size choices. A search of seven databases (2010 to April 2019) provided the records for title and abstract screening, with relevant articles assessed for eligibility in the full-text. Fourteen articles met the inclusion criteria, with relevant data extracted by one reviewer and checked for consistency by a second reviewer. Twelve studies were conducted in North America, where the government regulates serving size information. Several studies reported a poor understanding of serving size labelling. Indeed, consumers interpreted the labelled serving size as a recommended serving for dietary guidelines for healthy eating rather than a typical consumption unit, which is set by the manufacturer or regulated in some countries such as in the U.S. and Canada. Not all studies assessed consumption; however, larger labelled serving sizes resulted in larger self-selected portion sizes in three studies. However, another study performed on confectionary reported the opposite effect, with larger labelled serving sizes leading to reduced consumption. The limited number of included studies showed that labelled serving size affects portion size selection and consumption, and that any labelled serving size format changes may result in increased portion size selection, energy intake and thus contribute to the rise of the prevalence of overweight and obesity. Research to test cross-continentally labelled serving size format changes within experimental and natural settings (e.g., at home) are needed. In addition, tailored, comprehensive and serving-size-specific food literacy initiatives need to be evaluated to provide recommendations for effective serving size labelling. This is required to ensure the correct understanding of nutritional content, as well as informing food choices and consumption, for both core foods and discretionary foods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".